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Oriented-tooth recognition using a five-axis object-detection approach

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Abstract

X-ray images are essential data sources for checking the condition of the teeth, gums, jaws, and bone structure of the mouth. Tooth recognition is fundamental in image-processing-based diagnoses. In most previous recognition studies, only four-axis-based object-detection models have been considered because they perform normal object detection while the object is resting on a flat surface. However, because the teeth have various orientations, the existing four-axis-based model leads to inaccurate and inefficient recognition results. Thus, in this study, we propose a five-axis-based object-detection model that considers the orientation of the tooth. Based on a tooth-image dataset labeled using the five-axis ground truth, our proposed method processed five-axis annotated data by employing a variant of the faster region-based convolutional neural network. In the experiment, our proposed method outperformed the existing four-axis approach, both qualitatively and quantitatively. The experimental results indicated that the proposed five-axis-based recognition model will be an important basis for a dental-image-based diagnosis.

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Acknowledgments

This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. 2020R1F1A1067914).

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Authors and Affiliations

  1. Department of Data Science, Seoul National University of Science and Technology, 232, Gongneung-ro, Nowon-gu, Seoul, 01811, Republic of Korea

    Jonghwan Park

  2. Department of Industrial Engineering, Seoul National University of Science and Technology, 232, Gongneung-ro, Nowon-gu, Seoul, 01811, Republic of Korea

    Younghoon Lee

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  1. Jonghwan Park
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Correspondence to Younghoon Lee.

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Park, J., Lee, Y. Oriented-tooth recognition using a five-axis object-detection approach. Appl Intell 53, 9846–9857 (2023). https://doi.org/10.1007/s10489-022-03544-x

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  • DOI: https://doi.org/10.1007/s10489-022-03544-x

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